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Training

Prepare Dataset

Example data processing scripts, and you may tailor your own one along with a Dataset class in src/f5_tts/model/dataset.py.

1. Some specific Datasets preparing scripts

Download corresponding dataset first, and fill in the path in scripts.

# Prepare the Emilia dataset
python src/f5_tts/train/datasets/prepare_emilia.py

# Prepare the Wenetspeech4TTS dataset
python src/f5_tts/train/datasets/prepare_wenetspeech4tts.py

# Prepare the LibriTTS dataset
python src/f5_tts/train/datasets/prepare_libritts.py

# Prepare the LJSpeech dataset
python src/f5_tts/train/datasets/prepare_ljspeech.py

2. Create custom dataset with metadata.csv

Use guidance see #57 here.

python src/f5_tts/train/datasets/prepare_csv_wavs.py

Training & Finetuning

Once your datasets are prepared, you can start the training process.

1. Training script used for pretrained model

# setup accelerate config, e.g. use multi-gpu ddp, fp16
# will be to: ~/.cache/huggingface/accelerate/default_config.yaml     
accelerate config

# .yaml files are under src/f5_tts/configs directory
accelerate launch src/f5_tts/train/train.py --config-name F5TTS_Base_train.yaml

# possible to overwrite accelerate and hydra config
accelerate launch --mixed_precision=fp16 src/f5_tts/train/train.py --config-name F5TTS_Small_train.yaml ++datasets.batch_size_per_gpu=19200

2. Finetuning practice

Discussion board for Finetuning #57.

Gradio UI training/finetuning with src/f5_tts/train/finetune_gradio.py see #143.

The use_ema = True is harmful for early-stage finetuned checkpoints (which goes just few updates, thus ema weights still dominated by pretrained ones), try turn it off and see if provide better results.

3. Wandb Logging

The wandb/ dir will be created under path you run training/finetuning scripts.

By default, the training script does NOT use logging (assuming you didn't manually log in using wandb login).

To turn on wandb logging, you can either:

  1. Manually login with wandb login: Learn more here
  2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/site/ and set the environment variable as follows:

On Mac & Linux:

export WANDB_API_KEY=<YOUR WANDB API KEY>

On Windows:

set WANDB_API_KEY=<YOUR WANDB API KEY>

Moreover, if you couldn't access Wandb and want to log metrics offline, you can the environment variable as follows:

export WANDB_MODE=offline